Generating a metrology recipe usable for examination of a semiconductor specimen

ABSTRACT

There is provided a system and method of generating a metrology recipe usable for examining a semiconductor specimen, comprising: obtaining a first image set comprising a plurality of first images captured by an examination tool, obtaining a second image set comprising a plurality of second images, wherein each second image is simulated based on at least one first image, wherein each second image is associated with ground truth data; performing a first test on the first image set and a second test on the second image set in accordance with a metrology recipe configured with a first parameter set, and determining, in response to a predetermined criterion not being met, to select a second parameter set, configure the metrology recipe with the second parameter set, and repeat the first test and the second test in accordance with the metrology recipe configured with the second parameter set.

TECHNICAL FIELD

The presently disclosed subject matter relates, in general, to the fieldof examination of a semiconductor specimen, and more specifically, tometrology recipe generation usable for the examination of a specimen.

BACKGROUND

Current demands for high density and performance associated with ultralarge-scale integration of fabricated devices require submicronfeatures, increased transistor and circuit speeds, and improvedreliability. As semiconductor processes progress, pattern dimensionssuch as line width, and other types of critical dimensions, arecontinuously shrunken. Such demands require formation of device featureswith high precision and uniformity, which, in turn, necessitates carefulmonitoring of the fabrication process, including automated examinationof the devices while they are still in the form of semiconductor wafers.

Examination can be provided by using non-destructive examination toolsduring or after manufacture of the specimen to be examined. Examinationgenerally involves generating certain output (e.g., images, signals,etc.) for a specimen by directing light or electrons to the wafer anddetecting the light or electrons from the wafer. A variety ofnon-destructive examination tools includes, by way of non-limitingexample, scanning electron microscopes, atomic force microscopes,optical inspection tools, etc.

Examination processes can include a plurality of examination steps.During the manufacturing process, the examination steps can be performeda multiplicity of times, for example after the manufacturing orprocessing of certain layers, or the like. Additionally oralternatively, each examination step can be repeated multiple times, forexample for different wafer locations or for the same wafer locationswith different examination settings.

Examination processes are used at various steps during semiconductorfabrication to detect and classify defects on specimens, as well asperform metrology related operations. Effectiveness of examination canbe increased by automatization of process(es) as, for example, defectdetection, Automatic Defect Classification (ADC), Automatic DefectReview (ADR), automated metrology-related operations, etc.

SUMMARY

In accordance with certain aspects of the presently disclosed subjectmatter, there is provided a computerized system of generating ametrology recipe usable for examining a semiconductor specimen, thesystem comprising a processor and memory circuitry (PMC) configured for:obtaining a first image set comprising a plurality of first imagescaptured by an examination tool, each first image informative of atleast one first image structural element (ISE) representing at least onestructural element (SE) on the semiconductor specimen; obtaining asecond image set comprising a plurality of second images, wherein eachsecond image is simulated based on at least one first image andinformative of at least one second ISE representing the at least one SE,wherein the at least one second ISE in each second image is resized to arespective scale with reference to the at least one first ISE, andwherein each second image is associated with ground truth data relatedto the respective scale of the at least one second ISE; performing afirst test on the first image set, comprising: performing a metrologyoperation on the first image set in accordance with a metrology recipeconfigured with a first parameter set, giving rise to a plurality offirst measurements corresponding to the plurality of first images, andcalculating a first score indicative of precision of the metrologyrecipe based on the plurality of first measurements; performing a secondtest on the second image set, comprising: performing the metrologyoperation on the second image set in accordance with the metrologyrecipe, giving rise to a plurality of second measurements correspondingto the plurality of second images, and calculating a second scoreindicative of sensitivity of the metrology recipe based on the pluralityof second measurements with respect to the associated ground truth data;and determining, in response to a predetermined criterion related to thefirst score and the second score not being met, to select a secondparameter set, configure the metrology recipe with the second parameterset, and repeat the first test and the second test in accordance withthe metrology recipe configured with the second parameter set.

In addition to the above features, the system according to this aspectof the presently disclosed subject matter can comprise one or more offeatures (i) to (x) listed below, in any desired combination orpermutation which is technically possible:

-   (i). The plurality of first images are captured from one or more    sites on the semiconductor specimen.-   (ii). The PMC is further configured for generating the second image    set, comprising: generating a first design image based on the at    least one first image, the first design image informative of at    least one design structural element (DSE) corresponding to the at    least one ISE and associated with first ground truth data related to    a first scale of the at least one DSE; generating one or more    additional design images each informative of the at least one DSE    resized to a respective scale with reference to the first scale,    giving rise to a plurality of design images comprising the first    design image and the additional design images associated with    respective ground truth data related to respective scales of the at    least one DSE; and using the plurality of design images to generate    the second image set, comprising: simulating, based on the plurality    of design images, one or more effects caused by one or more physical    processes of the semiconductor specimen, giving rise to the    plurality of second images associated with the respective ground    truth data.-   (iii). The simulating one or more effects comprises: performing a    first simulation on the plurality of design images to simulate    effects caused by a fabrication process of the semiconductor    specimen, giving rise to a plurality of first simulated images;    performing a second simulation on the plurality of first simulated    images to simulate effects caused by a scanning process of the    semiconductor specimen, giving rise to a plurality of second    simulated images; performing a third simulation on the plurality of    second images to simulate effects caused by a signal processing    process of the semiconductor specimen, giving rise to the plurality    of second images; and associating the plurality of second images    with the respective ground truth data.-   (iv). The PMC is configured to calculate the first score by    calculating variance among the plurality of first measurements based    on a precision measure.-   (v). The PMC is configured to calculate the second score by    estimating a linear regression function between the plurality of    second measurements and the associated ground truth data, and    obtaining the second score based on the estimated linear regression    function.-   (vi). The PMC is further configured to obtain a third image set    comprising at least one first image, and perform a third test on the    third image set, comprising: perform a metrology operation on the    third image set in accordance with the metrology recipe, and    calculate a third score indicative of throughput of the metrology    recipe based on duration of the metrology operation.-   (vii). The first parameter set comprises a plurality of recipe    parameters assigned with first values, the recipe parameters    selected from a group comprising: measurement algorithm parameters    and image generation parameters.-   (viii). The second parameter set comprises the plurality of recipe    parameters assigned with second values which are selected    automatically using an optimization method.-   (ix). The PMC is further configured to repeat the first test and the    second test one or more times in accordance with the metrology    recipe configured with one or more additional parameter sets, until    the predetermined criterion is met, thereby generating a metrology    recipe usable for runtime examination of a semiconductor specimen.-   (x). The metrology recipe is defined in accordance with a metrology    application selected from a group comprising: Measurement-Based    Inspection (MBI), Critical Dimension Uniformity (CDU), Lithography    process control, CAD Awareness (CADA) and Overlay (OVL).

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a method for generating a metrology recipeusable for examining a semiconductor specimen, the method performed by aprocessor and memory circuitry (PMC) and comprising: obtaining a firstimage set comprising a plurality of first images captured by anexamination tool, each first image informative of at least one firstimage structural element (ISE) representing at least one structuralelement (SE) on the semiconductor specimen; obtaining a second image setcomprising a plurality of second images, wherein each second image issimulated based on at least one first image and informative of at leastone second ISE representing the at least one SE, wherein the at leastone second ISE in each second image is resized to a respective scalewith reference to the at least one first ISE, and wherein each secondimage is associated with ground truth data related to the respectivescale of the at least one second ISE; performing a first test on thefirst image set, comprising: performing a metrology operation on thefirst image set in accordance with a metrology recipe configured with afirst parameter set, giving rise to a plurality of first measurementscorresponding to the plurality of first images, and calculating a firstscore indicative of precision of the metrology recipe based on theplurality of first measurements; performing a second test on the secondimage set, comprising: performing the metrology operation on the secondimage set in accordance with the metrology recipe, giving rise to aplurality of second measurements corresponding to the plurality ofsecond images, and calculating a second score indicative of sensitivityof the metrology recipe based on the plurality of second measurementswith respect to the associated ground truth data; and determining, inresponse to a predetermined criterion related to the first score and thesecond score not being met, to select a second parameter set, configurethe metrology recipe with the second parameter set, and repeat the firsttest and the second test in accordance with the metrology recipeconfigured with the second parameter set.

This aspect of the disclosed subject matter can comprise one or more offeatures (i) to (x) listed above with respect to the system, mutatismutandis, in any desired combination or permutation which is technicallypossible.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a non-transitory computer readable mediumcomprising instructions that, when executed by a computer, cause thecomputer to perform a method for generating a metrology recipe usablefor examining a semiconductor specimen, the method comprising: obtaininga first image set comprising a plurality of first images captured by anexamination tool, each first image informative of at least one firstimage structural element (ISE) representing at least one structuralelement (SE) on the semiconductor specimen; obtaining a second image setcomprising a plurality of second images, wherein each second image issimulated based on at least one first image and informative of at leastone second ISE representing the at least one SE, wherein the at leastone second ISE in each second image is resized to a respective scalewith reference to the at least one first ISE, and wherein each secondimage is associated with ground truth data related to the respectivescale of the at least one second ISE; performing a first test on thefirst image set, comprising: performing a metrology operation on thefirst image set in accordance with a metrology recipe configured with afirst parameter set, giving rise to a plurality of first measurementscorresponding to the plurality of first images, and calculating a firstscore indicative of precision of the metrology recipe based on theplurality of first measurements; performing a second test on the secondimage set, comprising: performing the metrology operation on the secondimage set in accordance with the metrology recipe, giving rise to aplurality of second measurements corresponding to the plurality ofsecond images, and calculating a second score indicative of sensitivityof the metrology recipe based on the plurality of second measurementswith respect to the associated ground truth data; and determining, inresponse to a predetermined criterion related to the first score and thesecond score not being met, to select a second parameter set, configurethe metrology recipe with the second parameter set, and repeat the firsttest and the second test in accordance with the metrology recipeconfigured with the second parameter set.

This aspect of the disclosed subject matter can comprise one or more offeatures (i) to (x) listed above with respect to the system, mutatismutandis, in any desired combination or permutation which is technicallypossible.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the disclosure and to see how it may be carriedout in practice, embodiments will now be described, by way ofnon-limiting example only, with reference to the accompanying drawings,in which:

FIG. 1 illustrates a generalized block diagram of an examination systemin accordance with certain embodiments of the presently disclosedsubject matter.

FIG. 2A illustrates a generalized flowchart of generating a metrologyrecipe usable for examination of a semiconductor specimen in accordancewith certain embodiments of the presently disclosed subject matter.

FIG. 2B illustrates a generalized flowchart of a generalized flowchartof performing a third test in accordance with certain embodiments of thepresently disclosed subject matter.

FIG. 3A illustrates a generalized flowchart of generating the secondimage set in accordance with certain embodiments of the presentlydisclosed subject matter.

FIG. 3B illustrates a generalized flowchart of an exemplary simulationprocess used to generating the second images in accordance with certainembodiments of the presently disclosed subject matter.

FIG. 4 illustrates an example of a first design image in accordance withcertain embodiments of the presently disclosed subject matter.

FIG. 5 illustrates an example of additional design images in accordancewith certain embodiments of the presently disclosed subject matter.

FIG. 6 illustrates exemplary simulated images generated using thesimulation process described with reference to FIG. 3B in accordancewith certain embodiments of the presently disclosed subject matter.

FIG. 7 illustrates an example of estimation of linear regression inaccordance with certain embodiments of the presently disclosed subjectmatter.

FIG. 8 illustrates an example of selecting the optimal parameter setusing a Nelder-Mead optimization method in accordance with certainembodiments of the presently disclosed subject matter.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the disclosure.However, it will be understood by those skilled in the art that thepresently disclosed subject matter may be practiced without thesespecific details. In other instances, well-known methods, procedures,components and circuits have not been described in detail so as not toobscure the presently disclosed subject matter.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “generating”, “simulating”,“obtaining”, “examining”, “performing”, “calculating”, “determining”,“selecting”, “configuring”, “repeating”, “using”, “associating”,“estimating”, or the like, refer to the action(s) and/or process(es) ofa computer that manipulate and/or transform data into other data, saiddata represented as physical, such as electronic, quantities and/or saiddata representing the physical objects. The term “computer” should beexpansively construed to cover any kind of hardware-based electronicdevice with data processing capabilities including, by way ofnon-limiting example, the examination system, the metrology recipegeneration system and respective parts thereof disclosed in the presentapplication.

The term “examination” used in this specification should be expansivelyconstrued to cover any kind of metrology-related operations, as well asoperations related to detection and/or classification of defects in aspecimen during its fabrication. Examination is provided by usingnon-destructive examination tools during or after manufacture of thespecimen to be examined. By way of non-limiting example, the examinationprocess can include runtime scanning (in a single or in multiple scans),sampling, reviewing, measuring, classifying and/or other operationsprovided with regard to the specimen or parts thereof, using the same ordifferent inspection tools. Likewise, examination can be provided priorto manufacture of the specimen to be examined, and can include, forexample, generating an examination recipe(s) and/or other setupoperations. It is noted that, unless specifically stated otherwise, theterm “examination” or its derivatives used in this specification are notlimited with respect to resolution or size of an inspection area. Avariety of non-destructive examination tools includes, by way ofnon-limiting example, scanning electron microscopes, atomic forcemicroscopes, optical inspection tools, etc.

The term “metrology” used in this specification should be expansivelyconstrued to cover any kind of measuring characteristics and features ina specimen provided by using examination and/or metrology tools duringor after manufacture of the specimen to be inspected. By way ofnon-limiting example, the metrology process can include generating ameasurement recipe and/or performing runtime measurement, for example byscanning (in a single or in multiple scans), reviewing, measuring and/orother operations provided with regard to the specimen or parts thereofusing the same or different tools. Measurement results such as measuredimages are analyzed for example, by employing image-processingtechniques. Note that, unless specifically stated otherwise, the term“metrology” or derivatives thereof used in this specification are notlimited with respect to measurement technology, measurement resolutionor size of inspection area.

The terms “non-transitory memory” and “non-transitory storage medium”used herein should be expansively construed to cover any volatile ornon-volatile computer memory suitable to the presently disclosed subjectmatter.

The term “specimen” used in this specification should be expansivelyconstrued to cover any kind of wafer, masks, and other structures,combinations and/or parts thereof used for manufacturing semiconductorintegrated circuits, magnetic heads, flat panel displays, and othersemiconductor-fabricated articles.

The term “defect” used in this specification should be expansivelyconstrued to cover any kind of abnormality or undesirable feature formedon or within a specimen.

The term “design data” used in the specification should be expansivelyconstrued to cover any data indicative of hierarchical physical design(layout) of a specimen. Design data can be provided by a respectivedesigner and/or can be derived from the physical design (e.g. throughcomplex simulation, simple geometric and Boolean operations, etc.).Design data can be provided in different formats as, by way ofnon-limiting examples, GDSII format, OASIS format, etc. Design data canbe presented in vector format, grayscale intensity image format, orotherwise.

It is appreciated that, unless specifically stated otherwise, certainfeatures of the presently disclosed subject matter, which are describedin the context of separate embodiments, can also be provided incombination in a single embodiment. Conversely, various features of thepresently disclosed subject matter, which are described in the contextof a single embodiment, can also be provided separately or in anysuitable sub-combination. In the following detailed description,numerous specific details are set forth in order to provide a thoroughunderstanding of the methods and apparatus.

Bearing this in mind, attention is drawn to FIG. 1 illustrating afunctional block diagram of an examination system in accordance withcertain embodiments of the presently disclosed subject matter.

The examination system 100 illustrated in FIG. 1 can be used forexamination of a semiconductor specimen (e.g. of a wafer and/or partsthereof) as part of the specimen fabrication process. According tocertain embodiments of the presently disclosed subject matter, theillustrated examination system 100 comprises a computer-based system 101capable of automatically generating a metrology recipe using imagesobtained during specimen fabrication (referred to hereinafter asfabrication process (FP) images). System 101 is thus also referred to asa recipe generation system in the present disclosure. System 101 can beoperatively connected to one or more examination tools 120. Themetrology recipe as generated by system 101 can be used by theexamination tool(s) 120 for examination of a semiconductor specimen. Insome embodiments, at least one of the examination tools 120 hasmetrology capabilities and can be configured to capture FP images andperform metrology operations on the captured images. Such an examinationtool is also referred to as a metrology tool.

By way of example, FP images that can be used for generating the recipecan be selected from images of a specimen (e.g. wafer or parts thereof)captured during the manufacturing process, derivatives of the capturedimages obtained by various pre-processing stages (e.g. images of a partof a wafer or a photomask captured by a scanning electron microscope(SEM) or an optical inspection system, registered images of differentexamination modalities corresponding to the same mask location,segmented images, height map images, etc.) and computer-generated designdata-based images. It is to be noted that in some cases the images caninclude image data (e.g. captured images, processed images, etc.) andassociated numeric data (e.g. metadata, hand-crafted attributes, etc.).It is further noted that image data can include data related to a layerof interest and/or to one or more other layers of the specimen.

The term “examination tool(s)” used herein should be expansivelyconstrued to cover any tools that can be used in examination-relatedprocesses, including, by way of non-limiting example, imaging, scanning(in a single or in multiple scans), sampling, reviewing, measuring,classifying and/or other processes provided with regard to the specimenor parts thereof.

By way of example, a specimen can be examined by one or morelow-resolution examination tools (e.g. an optical inspection system,low-resolution SEM, etc.). The resulting data (referred to aslow-resolution image data), informative of low-resolution images of thespecimen, can be transmitted—directly or via one or more intermediatesystems—to system 101. Alternatively, or additionally, the specimen canbe examined by a high-resolution tool (e.g. a scanning electronmicroscope (SEM) or Atomic Force Microscopy (AFM) or TransmissionElectron Microscope (TEM)). The resulting data (referred to ashigh-resolution image data), informative of high-resolution images ofthe specimen, can be transmitted—directly or via one or moreintermediate systems—to system 101.

Without limiting the scope of the disclosure in any way, it should alsobe noted that the examination tools 120 can be implemented asexamination machines of various types, such as optical imaging machines,electron beam inspection machines, and so on. In some cases, the sameexamination tool can provide low-resolution image data andhigh-resolution image data.

System 101 includes a processor and memory circuitry (PMC) 102operatively connected to a hardware-based I/O interface 126. PMC 102 isconfigured to provide processing necessary for operating the system asfurther detailed with reference to FIGS. 2A, 2B, 3A and 3B, andcomprises a processor (not shown separately) and a memory (not shownseparately). The processor of PMC 102 can be configured to executeseveral functional modules in accordance with computer-readableinstructions implemented on a non-transitory computer-readable memorycomprised in the PMC. Such functional modules are referred tohereinafter as comprised in the PMC.

As aforementioned, system 101 is configured to automatically generate ametrology recipe (in particular, a parameter set) usable for examining asemiconductor specimen. The term “metrology recipe” should beexpansively construed to cover any recipe that can be used by anexamination tool (or more specifically, a metrology tool) for performingmetrology operations during a runtime measurement phase. The term“metrology operation” used in this specification should be expansivelyconstrued to cover any metrology operation procedure used to extractmetrology information relating to one or more structural elements on asemiconductor specimen. The term “parameter set” should be used to coverany set of parameters related to metrology operations. By way ofexample, metrology information to be extracted can be indicative of oneor more of the following: dimensions (e.g., line widths, line spacing,contacts diameters, size of the element, edge roughness, gray levelstatistics, etc.), shapes of elements, distances within or betweenelements, related angles, overlay information associated with elementscorresponding to different design levels, etc. The metrology operationscan include measurement operations which can include structure-basedmeasurements, rule-based measurements, measurements based on templates,measurements associated with geometric properties such as distances andangles, and/or other measurements.

Metrology recipe parameters are conventionally tuned manually at thecustomer site, which is normally performed under time pressure. Suchmanual tuning is time-consuming. Typically, the recipe performance testneeds to run on thousands of combinations of parameter values ofcritical recipe parameters, for which an exhaustive parameter search isperformed, which is a recipe creation throughput (TPT) killer. Inaddition, such manual tuning requires expert level of applicationknowledge, which adds another layer of difficulty for the customer toensure the quality of the manual work.

Due to these difficulties, currently only a precision test is performedto evaluate the precision measure of the recipe performance. Anotherimportant performance measure, sensitivity, is not tested, since it iscomplicated, and it is not always possible to perform a sensitivity test“on the fly” during the recipe creation. This is at least partiallybecause it is problematic to obtain the ground truth data for the FPimages in the FAB environment. For obtaining ground truth data, areference tool is typically needed, which can be destructive. Therefore,even after performing the precision test, the sensitivity performance ofthe tuned recipe is often still not good, and the trade-off between thesensitivity vs. precision measure is not satisfying.

Certain embodiments of the present disclosure propose a system andmethod of automatic metrology recipe creation which not only automatesthe recipe tuning/optimization process, but also enables the sensitivitytest to be performed in addition to the precision test, as will bedescribed below in further detail with reference to FIGS. 2A, 2B, 3A and3B.

According to certain embodiments, functional modules comprised in PMC102 can include a test set generator 104, a precision test module 106, asensitivity test module 108 and a determination module 110. The PMC 102can be configured to obtain, via I/O interface 126, a first image setcomprising a plurality of first images captured by an examination tool.Each first image is informative of at least one first image structuralelement (ISE) representing at least one structural element (SE) on thesemiconductor specimen. The PMC 102 can be further configured to obtain,via I/O interface 126, a second image set comprising a plurality ofsecond images. Each second image can be simulated based on at least onefirst image and informative of at least one second ISE representing theat least one SE. The at least one second ISE in each second image isresized to a respective scale with reference to the at least one firstISE. Each second image is associated with ground truth data related tothe respective scale of the at least one second ISE.

The precision test module 106 can be configured to perform a first teston the first image set, comprising: perform a metrology operation on thefirst image set in accordance with a metrology recipe configured with afirst parameter set, giving rise to a plurality of first measurementscorresponding to the plurality of first images, and calculate a firstscore indicative of precision of the metrology recipe based on theplurality of first measurements.

The sensitivity test module 108 can be configured to perform a secondtest on the second image set, comprising: perform the metrologyoperation on the second image set in accordance with the metrologyrecipe, giving rise to a plurality of second measurements correspondingto the plurality of second images, and calculate a second scoreindicative of sensitivity of the metrology recipe based on the pluralityof second measurements with respect to the associated ground truth data.

The determination module 110 can be configured to determine, in responseto the predetermined criterion not being met, to select a secondparameter set, configure the metrology recipe with the second parameterset, and repeat the first test and the second test in accordance withthe metrology recipe configured with the second parameter set.

Operations of system 101, PMC 102 and the functional modules therein,will be further detailed with reference to FIGS. 2A, 2B, 3A and 3B.

According to certain embodiments, system 101 can comprise a storage unit122. The storage unit 122 can be configured to store any data necessaryfor operating system 101, e.g., data related to input and output ofsystem 101, as well as intermediate processing results generated bysystem 101. By way of example, the storage unit 122 can be configured tostore the FP images and/or derivatives thereof produced by theexamination tool 120. Accordingly, the one or more FP images can beretrieved from the storage unit 122 and provided to the PMC 102 forfurther processing.

In some embodiments, system 101 can optionally comprise a computer-basedGraphical User Interface (GUI) 124 which is configured to enableuser-specified inputs related to system 101. For instance, the user canbe presented with a visual representation of the specimen (for example,by a display forming part of GUI 124), including image data of thespecimen. The user may be provided, through the GUI, with options ofdefining certain operation parameters. In some cases the user may alsoview operation results on the GUI.

As will be further detailed with reference to FIGS. 2A and 2B, system101 is configured to receive, via I/O interface 126, FP input data. FPinput data can include data (and/or derivatives thereof and/or metadataassociated therewith) produced by the examination tools 120 and/or datastored in one or more data depositories. It is noted that in some casesFP input data can include image data (e.g. captured images, imagesderived from the captured images, simulated images, synthetic images,etc.) and associated numeric data (e.g. metadata, hand-craftedattributes, etc.). It is further noted that image data can include datarelated to a layer of interest and/or to one or more other layers of thespecimen.

System 101 is further configured to process the received FP input dataand send, via I/O interface 126, the results or part thereof (e.g., thegenerated metrology recipe) to the storage unit 122, and/or theexamination tool 120.

In some embodiments, additionally to the examination tool 120, theexamination system 100 can comprise one or more examination modules,such as, e.g., defect detection module and/or Automatic Defect ReviewModule (ADR) and/or Automatic Defect Classification Module (ADC) and/ora metrology-related module and/or other examination modules which areusable for examination of a semiconductor specimen. The one or moreexamination modules can be implemented as stand-alone computers, ortheir functionalities (or at least part thereof) can be integrated withthe examination tool 120. In some embodiments, the generated metrologyrecipe can be used by the examination tool 120 and/or the one or moreexamination modules (or part thereof) for examination of the specimen.

For purpose of illustration only, certain embodiments of the followingdescription are provided for generating a metrology recipe usable formetrology-related examination of a semiconductor specimen. Those skilledin the art will readily appreciate that the teachings of the presentlydisclosed subject matter are applicable to various examinations such as,for example, defect detection, ADR, ADC, and alike.

Those versed in the art will readily appreciate that the teachings ofthe presently disclosed subject matter are not bound by the systemillustrated in FIG. 1; equivalent and/or modified functionality can beconsolidated or divided in another manner and can be implemented in anyappropriate combination of software with firmware and/or hardware.

It is noted that the examination system illustrated in FIG. 1 can beimplemented in a distributed computing environment, in which theaforementioned functional modules as comprised in the PMC 102 can bedistributed over several local and/or remote devices, and can be linkedthrough a communication network. It is further noted that in otherembodiments at least some of the examination tool(s) 120, storage unit122 and/or GUI 124 can be external to the examination system 100 andoperate in data communication with system 101 via I/O interface 126.System 101 can be implemented as stand-alone computer(s) to be used inconjunction with the examination tools. Alternatively, the respectivefunctions of the system 101 can, at least partly, be integrated with oneor more examination tools 120, thereby facilitating and enhancing thefunctionalities of the examination tools 120 in examination-relatedprocesses.

Referring to FIG. 2A, there is illustrated a generalized flowchart ofgenerating a metrology recipe usable for examination of a semiconductorspecimen in accordance with certain embodiments of the presentlydisclosed subject matter.

A first image set can be obtained (202) (e.g., by the PMC 102 via I/Ointerface 126). The first image set comprises a plurality of firstimages captured by an examination tool. Each first image is informativeof at least one first image structural element (ISE) representing atleast one structural element (SE) on the semiconductor specimen.

The first images can be FP images of a semiconductor specimen capturedby an examination tool. By way of example, the first images can becaptured by a low-resolution examination tool (e.g. an opticalinspection system, low-resolution SEM, etc.). Alternatively, the firstimages can be captured by a high-resolution examination tool (e.g.,high-resolution SEM, AFM, TEM, etc.). A structural element (SE) usedherein can refer to any original object on the specimen that has ageometrical shape or geometrical structure with a contour, in some casescombined with other object(s). Examples of structural elements caninclude general shape features, including, such as, e.g., contacts,line/space structures, etc. An image structural element (ISE) refers tothe image representation of the SE in the captured images, and can bepresented, e.g., in the form of a polygon.

In some embodiments, the first images can be SEM images captured by aSEM tool, each first image representing at least part of the specimen(e.g., a part of a die of a wafer). In some cases, the first images arecaptured from one or more sites/fields on the specimen. For instance,the first image set can comprise a plurality of subsets of imagescaptured from a plurality of sites on the specimen that share the samedesign pattern (e.g., the sites are located at the same place in thedies). The first image set can be used for performing a first test(i.e., precision test), as described below with reference to block 204.

A second image set can be obtained (212) (e.g., by the PMC 102 via I/Ointerface 126). The second image set comprises a plurality of secondimages. Each second image is simulated based on at least one first imageand is informative of at least one second ISE representing the at leastone SE on the specimen. In other words, the second image is a simulatedimage generated by performing simulation based on the at least one firstimage. The at least one second ISE in each second image is resized to arespective scale with reference to the at least one first ISE, and eachsecond image is associated with ground truth data related to therespective scale of the at least one second ISE. The second image setcan be used for performing a second test (i.e., sensitivity test), asdescribed below with reference to block 214.

FIG. 3A illustrates a generalized flowchart of generating the secondimage set in accordance with certain embodiments of the presentlydisclosed subject matter. The second image set can be generated, e.g.,by an image simulator module (not illustrated) as comprised in the testset generator 104.

A first design image can be generated (302) based on at least one firstimage. The first design image is informative of at least one designstructural element (DSE) corresponding to the at least one ISE andassociated with first ground truth data related to a first scale (e.g.,geometric size) of the at least one DSE. Specifically, the first image(e.g., a SEM image) can be analyzed and feature extraction can beperformed on the first image. The extracted features can be used tosimulate a first design image corresponding to the first image. By wayof example, the first design image can be a simulated CAD image.

Referring now to FIG. 4, there is illustrated an example of a firstdesign image in accordance with certain embodiments of the presentlydisclosed subject matter. As shown, an exemplary first image—a SEM image402 is obtained, which comprises an image structural element (ISE) 403presented therein (exemplified in the shape of a column). Featureextraction is performed on the SEM image 402. By way of example, theextracted features can include one or more measurements related to theISE 403, such as, e.g., the width 405 of the ISE, etc. Other featuresmay include features representative of the structure and/or pattern ofthe ISE, such as, e.g., edges, corners, pixel intensities, etc. Asimulated design image 406 corresponding to the first image 402 can begenerated based on the extracted features. As shown, the design image406 comprises a design structural element (DSE) 407 corresponding to theISE 403. The DSE 407 has a first scale/size associated therewith, suchas, e.g., the width 408 thereof which corresponds to the width 405 ofthe ISE 403. The first scale can serve as first ground truth dataassociated with the DSE.

It is to be noted that the first scale of the DSE is not necessarily thesame as the scale of the ISD. It is also to be noted that although thewidth is used as an example of a measurement indicative of the geometricsize of the structural elements, this should not be regarded as limitingthe present disclosure in any way. Other suitable critical dimension(CD) measurements can be used in addition to or in lieu of the above.

Continuing with the description of FIG. 3A, one or more additionaldesign images can be generated (304), each informative of the at leastone DSE resized to a respective scale with reference to the first scale,giving rise to a plurality of design images comprising the first designimage and the additional design images associated with respective groundtruth data related to respective scales of the at least one DSE.

Referring now to FIG. 5, there is illustrated an example of additionaldesign images in accordance with certain embodiments of the presentlydisclosed subject matter. As shown, the first design image 406 asgenerated in the example of FIG. 4 is illustrated. Assume the width 405of the ISE in SEM image 402 is 10 nm, and the width 408 of the DSE inthe design image 406 is X nm (as described above, X is not necessarilyequal to 10 nm). Additional design images can be generated by changingthe size of the DSE. In the present example, the width can be resized toa plurality of different scales, such as, e.g., X−3, X−2, X−1, X+1, X+2,and X+3 nm, thereby giving rise to a plurality of additional designimages with the resized scales, as illustrated respectively in images501-506. Together with the design image 406, seven design images arecreated, each associated with a respective scale of the DSE therein.

Continuing with the description of FIG. 3A, the plurality of designimages can be used (306) to generate the second image set, comprising:simulating, on the plurality of design images, one or more effectscaused by one or more physical processes of the semiconductor specimen,giving rise to the plurality of second images associated with therespective ground truth data. By way of example, in cases where thefirst image is a real SEM image, the first design image is a simulatedCAD image based on the SEM image, and the second images are simulatedSEM images based on the simulated CAD images. According to certainembodiments, the effects can refer to variations caused by one or moreof the following physical processes: manufacturing/fabrication process(e.g., printing the design patterns of the specimen on the wafer by anoptical lithography tool), scanning process, signal processing processin the examination tool, etc.

FIG. 3B illustrates a generalized flowchart of an exemplary simulationprocess used to generate the second images in accordance with certainembodiments of the presently disclosed subject matter.

As shown, a first simulation is performed (310), where effects caused bythe fabrication process can be simulated, based on the plurality ofdesign images. The simulated images, after the first simulation (alsoreferred to as first simulated images), represent how the designpatterns in the design images would actually appear on the wafer. Inother words, the first simulation transfers the design intent layout tothe expected processed pattern on the wafer. Such simulation is alsoreferred as stepper simulation, and can be performed, e.g., byconvolving the CAD data (e.g., in the form of rasterized CAD) with astepper beam shape filter. The stepper simulation assumes a Gaussianshape of the stepper optical beam. For example, the patterns on thewafer can be defined as thresholding of convolution of the binary CADimage with a Gaussian filter simulating the stepper optical beam shape.

In some cases, process variation (PV) can be considered during the firstsimulation. Process variation can refer to variations caused by a changein the fabrication process of the specimen. By way of example, thefabrication process may cause slight shifting/scaling/distortion ofcertain structures/patterns between different images which results inpattern variation in the images. By way of another example, thefabrication process may cause thickness variation of the specimen, whichaffects reflectivity, thus in turn affecting gray level of the resultingimage. For instance, die-to-die material thickness variation can resultin a different reflectivity between two of the dies, which leads to adifferent background gray level value for the images of the two dies.

A second simulation can be performed (320), where effects caused by thescanning process can be simulated based on the first simulated images.The scanning process refers to the process when the specimen is scannedby the examination tool, thereby generating an examination signal. Thesimulated images after the second simulation (also referred to as secondsimulated images) are representative of the examination signal asgenerated by yield of electrons from the specimen and prior to enteringthe detector for further signal processing. By way of example, thespecimen can be scanned by a SEM beam of a SEM tool, thereby obtaining aSEM signal which enters the SEM detector. The second simulation, in suchcases, aims to simulate the SEM signal entering the SEM detector. Asknown, a SEM beam has a Gaussian shape. By way of example, the SEMsignal can be obtained by a convolution of wafer Electron Yield(represented by a signal from hypothetic zero width electron beam) withthe SEM beam (represented by a SEM point spread function (PSF)). Thewafer Electron Yield can be defined based on wafer topography aspresented on the first simulated images. For simplicity, in some cases,it can be recognized that the wafer Electron Yield is proportional tothe patterns of the wafer topography on the first simulated images andis related to the material properties of the patterns.

Next, a third simulation can be performed (330), where effects caused bythe signal processing process can be simulated based on the secondsimulated images. The signal processing process refers to the signalprocessing path where the examination signal (e.g., the SEM signal) isprocessed by the signal processing module in the examination tool,giving rise to an output examination image (e.g., SEM image). The thirdsimulation reflects influence of the signal path on both signal andnoise. In some cases, it can be based on a generalized theory thatunifies stochastic, deterministic, continuous, and discrete behaviors.The simulated images after the third simulation (also referred to asthird simulated images) are the second images that constitute the secondimage set. By way of example, in cases where the specimen is examined bya SEM tool, the resulted second images from the above simulation processare simulated SEM images. Since the plurality of design images areassociated with respective ground truth data related to respectivescales of the at least one DSE, the second images which are generatedcorresponding to the plurality of design images can be also associated(340) with the respective ground truth data.

Continuing with the example in FIG. 5, as described above, each of theseven design images 501-506 and 406 is associated with a respectivescale of the DSE, e.g., the resized scales of the width of the DSE.Using the process as described with reference to FIG. 3B, sevencorresponding second images are generated. The respective scales of theDSE can be associated with the second images, which serve as groundtruth data associated therewith.

It is to be noted that although in the above example, the respectivescales are directly used as the ground truth data, in some other cases,the ground truth data can be measurement data related to the respectivescales. By way of example, the ground truth data can be a relativemeasurement with respect to multiple elements, such as, e.g., a distancebetween the DSE in the images of FIG. 5 and another DSE (notillustrated). Therefore, notwithstanding the examples described herein,which are non-limiting, the present disclosure is applicable to anyground truth data that is, or can be derived or related to therespective scales of the at least one second ISE.

FIG. 6 illustrates exemplary simulated images generated using thesimulation process described with reference to FIG. 3B in accordancewith certain embodiments of the presently disclosed subject matter.

As shown, image 602 is an example of a simulated design image generatedas described with reference to FIG. 4. Image 602 serves as the input ofthe first simulation described with reference to block 310 where theeffects caused by the fabrication process are simulated. The output ofthe first simulation is exemplified as image 604, illustrating how thedesign layout in the design image 602 would be expected to look like onthe wafer after the fabrication process. The second simulation, asdescribed above with reference to block 320, is performed on image 604,where the effects caused by the scanning process by a SEM tool aresimulated. The output of the second simulation is exemplified as image606, representing the SEM signal as reflected from the specimen andprior to entering the SEM detector for further signal processing. Theimage 606 is then provided as input for the third simulation, where asimulated SEM image 608 is generated after simulating the SEM signalpath, as described above with reference to block 330.

It is to be noted that the above described simulated effects withreference to FIGS. 3B and 6 are illustrated for exemplary purposes, andshould not be regarded as limiting the present disclosure in any way.Other possible effects (e.g., noise, focusing error, shadow effects,charging effects, etc.), and/or effects caused by other physicalprocesses, can be simulated in addition to or in lieu of the above forgenerating the second images.

It is also be noted that in some embodiments, the second image setgeneration as described with reference to FIG. 3A can be performed bythe test set generator 104 as comprised in the PMC of system 101. Insuch cases, the functionality of the test set generation is integratedwithin system 101. Alternatively, in some other embodiments, thefunctionality of the test set generation, or at least part thereof, canbe implemented in a separate computer system, and the resulted test setcan be sent to system 101 for further processing.

Referring back to FIG. 2A, as described above, the first image set isused for performing a first test (i.e., precision test), and the secondimage set, which is generated as described above, is used for performinga second test (i.e., sensitivity test).

Specifically, the first test can be performed (204) (e.g., by theprecision test module 106) on the first image set. The first testcomprises: performing (206) a metrology operation on the first image setin accordance with a metrology recipe configured with a first parameterset, giving rise to a plurality of first measurements corresponding tothe plurality of first images, and calculating (208) a first scoreindicative of precision of the metrology recipe based on the pluralityof first measurements.

Precision refers to the closeness of agreement between independentmeasurements on the same feature of a specimen. By way of example, highprecision indicates that the independent measurements of the samefeature are repeatable (i.e., the measurements have small variance withone another and the measurement distribution is relatively close). Insome embodiments, precision can be regarded as measurementrepeatability. In some other embodiments, precision can comprise twocomponents: repeatability and reproducibility. Repeatability refers to ameasure of measurement result distribution, where consecutivemeasurements are conducted repeatedly on the same site of the specimen,without any operator intervention. The cause for variation withinrepeated measurements results can be mainly due to the statisticalnature of the tool signal (e.g., SEM signal), and the interpretation ofthe new set of signals by the measurement algorithm as comprised in therecipe. Reproducibility refers to another measure of measurement resultdistribution, where the measurements are obtained from different sitesof the same specimen at different times. It accounts for the othersources of variation between independent measurements: wafer alignment,SEM autofocus, pattern recognition, tool stability etc.

For performing the precision test, the first images in the first imageset are captured from one or more sites/fields of the specimen. By wayof example, the first image set can comprise a plurality of subsets ofimages captured from a plurality of sites on the specimen that share thesame design pattern (e.g., the sites are located at the same position indifferent dies). The plurality of sites can be selected to reflect thein-homogeneity across the specimen. For instance, ten sites can beselected from ten dies on the wafer, each containing the same structuralelement 403 as exemplified in FIG. 4. For each of the ten sites, ten SEMimages (similar to the SEM image 402 as exemplified in FIG. 4) can becaptured, giving rise to a total of 100 SEM images constituting thefirst image set which can be used for performing the precision test.

According to certain embodiments, the metrology recipe can be initiallyconfigured with a first parameter set. A metrology operation can beperformed on each of the plurality of first images in accordance withthe configured metrology recipe, giving rise to a plurality of firstmeasurements corresponding to the plurality of first images. Continuingwith the above example in FIG. 4, assume the metrology operation is tomeasure the width of the structural element 403, thus after performingthe metrology operation on the image set of 100 SEM images, 100 widthmeasurements can be obtained corresponding to the 100 SEM images.

The first score (i.e., precision score) can be obtained by calculatingvariance between the plurality of first measurements based on aprecision measure. The precision measure can be related to therepeatability and/or reproducibility of the measurements as describedabove. By way of example, a repeatability variance can be calculatedbased on the variance of the measurements obtained for each site. Forexample, for the ten measurements corresponding to one site on thespecimen, the repeatability variance can be calculated as:

$3*{\sqrt{\frac{1}{n - 2}}\left\lbrack {{\sum\left( {{y -} < y >} \right)^{2}} - \frac{\left\lbrack {\sum{\left( {{x -} < x >} \right)\left( {{y -} < y >} \right)}} \right\rbrack^{2}}{\sum\left( {{x -} < x >} \right)^{2}}} \right\rbrack}$

Where n is the sample size which in the present example is 10, xindicates the respective sites ranging from 1-10, y refers to therespective measurements of the 10 sites, and <x> and <y> refer torespective average values of x and y.

A reproducibility variance can be calculated based on the variance ofthe average site differences. For example, for the ten sites on thespecimen, the reproducibility variance can be calculated as: 3*√{squareroot over (Σ(y_i−<y>) {circumflex over ( )}2/(N−1))}, where y_i refersto an average CD value measured at i-site, and <y> refers to an averagevalue for ten measurements at ten sites. In some cases, the precisionscore can be calculated based on the repeatability variance. Forinstance, in the above example, a precision score can be calculated foreach site based on the ten measurements obtained from the ten SEM imagesfor the site. In some other cases, the precision score can be calculatedbased on the repeatability variance and reproducibility variance, e.g.,Precision [nm]=√{square root over (repeatability²+reproducibility²)}. Anexample of calculating a precision score is described in “CD-SEMPrecision—Improved Procedure & Analysis”, Proc. SPIE 3677, Metrology,Inspection, and Process Control for Microlithography XIII, which isincorporated herein in its entirety by reference.

As aforementioned, the metrology recipe used herein refers to any recipethat can be used by an examination tool (or more specifically, ametrology tool) for performing metrology operations in runtime. Ametrology recipe typically comprises a large number of differentparameters. In some cases, the parameter set as referred to herein cancomprise a set of critical parameters as selected from all the recipeparameters. For instance, the parameter set can comprise one or more ofthe following: measurement algorithm parameters (such as, e.g., featureextraction parameters) and image generation parameters, etc. The firstparameter set used herein refers to the parameter set where the recipeparameters are assigned with first values (e.g., values that areselected initially or by default). Similarly, the second parameter setused herein refers to the parameter set where the recipe parameters areassigned with second values (e.g., values that arere-determined/re-selected as compared to the first values).

In some embodiments, the metrology recipe can be defined in accordancewith a specific metrology application. A metrology application refers towhat a customer/user is interested to measure in general with respect tothe specimen. By way of non-limiting example, a metrology applicationcan be selected from a group of metrology applications comprising:Measurement-Based Inspection (MBI), Critical Dimension Uniformity (CDU),CAD Awareness (CADA), Overlay (OVL), and Lithography process control.MBI refers to defect inspection using measurement. One example of MBIcan be detection of etch residue at the bottom of a trench. CDU refersto uniformity measurement related to critical dimension. One example ofCDU can be to create a uniformity map of the offset between twocontacts. Overlay refers to measurement of the overlay shift betweenmultiple layer patterns. One example of overlay can be to find thenominal overlay error between two layers on the edge of the wafer. CADArefers to the integration of CAD into SEM-based defect inspection.Lithography process control refers to control of a lithography tool andmaterial roughness. Roughness control is one kind of lithography processcontrol and refers to control of material roughness, or elasticityinduced roughness.

Therefore, different metrology recipes which are defined with respect todifferent metrology applications can include different recipe parametersto be tuned during the recipe generation process (or recipe tuningprocess). The present disclosure is not limited to tuning of a specificmetrology recipe, or specific recipe parameters.

A second test (i.e., sensitivity test) can be performed (214) (e.g., bythe sensitivity test module 108) on the second image set. The secondtest comprises: performing (216) the metrology operation on the secondimage set in accordance with the metrology recipe, giving rise to aplurality of second measurements corresponding to the plurality ofsecond images, and calculating (218) a second score indicative ofsensitivity of the metrology recipe based on the plurality of secondmeasurements with respect to the associated ground truth data.

Sensitivity refers to how sensitive the measurements are with respect tochanges of sizes of the features of a specimen. By way of example, ifthe feature of the specimen (e.g., width of a structural element)changes from 10 nm to 10.1 nm, high sensitivity indicates that thecorresponding measurement should be sensitive to such change of scales,and the measurement result should reflect such change.

As described above, for the metrology recipe, it is currently notpossible to perform a sensitivity test “on the fly” during the recipecreation. This is because, for performing a sensitivity test, groundtruth data (e.g., the real changes) of the measurements, must be knownso as to be able to compare with the measurement data, and to seewhether the measurements indeed reflect such changes. However, in theFAB environment, it is problematic to obtain the ground truth data forthe FP images. In some cases, a reference tool, such as, e.g., X-SectionTEM, is needed which may be destructive to the specimen.

FIGS. 3A and 3B, as described above, propose an automatic way ofsimulating a second image set of examination images associated with theground truth data of the measurements, thereby enabling to perform thesensitivity test during recipe creation.

According to certain embodiments, once a plurality of secondmeasurements corresponding to the plurality of second images areobtained, as described with reference to block 216, a second score canbe calculated (218) by estimating a linear regression function betweenthe plurality of second measurements and the associated ground truthdata, and obtaining the second score based on the estimated linearregression function.

Turning now to FIG. 7, there is illustrated an example of estimation oflinear regression in accordance with certain embodiments of thepresently disclosed subject matter.

Continuing with the example in FIG. 5, using the process as describedwith reference to FIG. 3B, seven second images can be generatedcorresponding to the seven design images 501-506 and 406. The sevensecond images are associated with corresponding ground truth data, e.g.,the respective resized scales of the DSE. Therefore, for each secondimage, a pair of corresponding second measurement (denoted as “measuredCD” in FIG. 7) and ground truth data (denoted as “synthetic CD” in FIG.7) can be obtained, as illustrated in table 704. As shown, table 704includes seven pairs of measured CD and synthetic CD, corresponding tothe seven second images. For instance, a second measurement of 5.36 nmhas a corresponding ground truth data of 5 nm associated therewith, anda second measurement of 7.23 nm has a corresponding ground truth data of7 nm associated therewith. The linear regression, as estimated based onthe seven pairs of data, is illustrated in graph 702, where x axisrepresents the synthetic CD (ground truth data), and y axis representsthe measured CD (second measurement). By way of example, the linearregression can be estimated as, e.g., the second measurement=gain*groundtruth+offset. In the present example, the gain is estimated as 0.9984,and the offset is estimated as 0.312 nm.

By way of example, in the above exemplified linear regression function,an estimation of the gain equal to 1 indicates an ideal sensitivity ofthe plurality of second measurements. The second score (i.e., thesensitivity score) can be calculated as, e.g., the deviation of theestimated gain from 1, or the absolute value of such deviation. Forinstance, in the above example, the gain is estimated as 0.9984, and thesecond score can be calculated as the deviation of 0.9984 from 1, whichis 0.0016. In another example, the second score can be calculated as,e.g., exp (−(gain−1)²/sigma{circumflex over ( )}2) which providesmaximal score when the estimated gain equals 1, where sigma refers to anempirical parameter indicative of the sharpness of the score function.In a further example, average statistical deviation (e.g., R²) of theplurality of the measurements from the linear fit can be calculated andused as the second score. R² is the square of the Pearson correlationcoefficient which is a statistic that measures linear correlationbetween two variables X and Y. The R² can be interpreted as theproportion of the variance in y attributable to the variance in x in thelinear regression function.

Referring back to FIG. 2A, once the first score and the second score areboth obtained, it can be determined (220) (e.g., by the determinationmodule 110 in FIG. 1), in response to a predetermined criterion relatedto the first score and the second score not being met, to select asecond parameter set, configure the metrology recipe with the secondparameter set, and repeat the first test and the second test asdescribed above with reference to blocks 204 and 214 in accordance withthe metrology recipe configured with the second parameter set, asillustrated with reference to block 222.

According to certain embodiments, the predetermined criterion can berelated to a precision criterion and a sensitivity criterion.Specifically, the first score (i.e., the precision score) can becompared with a predetermined precision requirement. The precisionrequirement can be predefined in accordance with the customer'sprecision requirement and/or based on previous examination experience.By way of example, the precision requirement can be based on a precisionthreshold to be compared with the precision score. A precision testresult can be obtained based on the comparison.

Similarly, the second score (i.e., the sensitivity score) can becompared with a predetermined sensitivity requirement. The sensitivityrequirement can be predefined in accordance with the customer'ssensitivity requirement and/or based on previous examination experience.By way of example, the sensitivity requirement can be based on asensitivity threshold to be compared with the sensitivity score. Asensitivity test result can be obtained based on the comparison.

In the above example, the sensitivity requirement can be set differentlyaccording to different ways of calculation of the sensitivity score. Forinstance, in cases where the sensitivity score is calculated as thedeviation of 0.9984 from 1, which is 0.0016, a sensitivity threshold canbe set as, e.g., 0.1, and the sensitivity criterion is regarded as beingmet when the sensitivity score does not exceed such a threshold. Inanother example, in cases where the sensitivity score is calculated as,e.g., exp (−(gain−1)²/sigma{circumflex over ( )}2) which providesmaximal score when the estimated gain equals 1, a sensitivity thresholdcan be set as, e.g., 0.9, and the sensitivity criterion is regarded asbeing met when the sensitivity score is not smaller than the threshold.

The predetermined criterion can be considered as being met when both theprecision criterion and the sensitivity criterion are satisfied. Incases where the predetermined criterion is met, the metrology recipeconfigured with the current parameter set is ready to be used forruntime examination of the specimen. In cases where the predeterminedcriterion is not met (e.g., either the precision criterion or thesensitivity criterion is not satisfied), which indicates the currentparameter set does not enable the recipe to provide satisfyingperformance, a second parameter set (i.e., the recipe parameters setwith second values) can be selected and used to configure the metrologyrecipe, and the first test and the second test are repeated as describedabove in accordance with the updated metrology recipe.

In some cases, the first test and the second test can be repeated one ormore times in accordance with the metrology recipe configured with oneor more additional parameter sets, until the predetermined criterion ismet, thereby generating a metrology recipe usable for runtimeexamination of a semiconductor specimen.

In some cases, in addition to precision and sensitivity, the recipeperformance measures can further include throughput. Throughput refersto the number of specimens examined in accordance with the recipe pertime unit under specified conditions (e.g., standard measurementconditions). In other words, the throughput measure can indicate thespeed of the examination using the recipe, or the time utilized toexamine one specimen. According to certain embodiments, additionally oralternatively, a third test (i.e., throughput test) can be performed toevaluate the performance of the recipe.

Referring to FIG. 2B, there is illustrated a generalized flowchart ofperforming a third test in accordance with certain embodiments of thepresently disclosed subject matter.

In order to test the throughput performance of the recipe (e.g., by athroughput test module (not illustrated in FIG. 1) comprised in the PMC102), a third image set can be obtained (222) and used for performingthe test. By way of example, the third image set can comprise at leastone of the first images as comprised in the first image set. A thirdtest can be performed (224) on the third image set, comprising:performing (226) a metrology operation on the third image set inaccordance with the metrology recipe, and calculating (228) a thirdscore indicative of throughput of the metrology recipe based on the timeduration of the metrology operation being performed.

According to certain embodiments, the throughput test in some cases canbe performed in addition to the precision test and the sensitivity test,and the determination of the predetermined criterion being met or not(as described with reference to block 220) can be made based on thethree scores instead of two scores. By way of example, the predeterminedcriterion can be related to a precision criterion, a sensitivitycriterion, and a throughput criterion. Specifically, the third score(i.e., the throughput score) can be compared with a predeterminedthroughput requirement. The throughput requirement can be predefined inaccordance with the customer's throughput requirement and/or based onprevious examination experience. For instance, the throughputrequirement can be based on a throughput threshold to be compared withthe throughput score. A throughput test result can be obtained based onthe comparison. The predetermined criterion can be considered as beingmet when the three criteria, i.e., the precision criterion, thesensitivity criterion, and the throughput criterion, are all satisfied.

In some other embodiments, the three tests can be selected and performedin any suitable combination, depending on the recipe performancemeasures selected by the customer. The present disclosure is not limitedby the number of tests and the specific tests performed for evaluatingthe recipe performance.

When the predetermined criterion is not met, a second parameter set isselected and the metrology recipe is configured with the secondparameter set. Considering the number of parameters comprised in theparameter set and the large amount of combinations of various possibleparameter values for different parameters (which form amulti-dimensional parameter space), an engineer has to choose whichvalues to assign to each recipe parameter. It is not easy to visualizethe impact of changing any specific parameter. Simulation of influenceon the measurement quality of such complicated parameter sets is oftenextremely computationally expensive to run, possibly taking upwards ofhours per execution. An exhaustive parameter search for selecting thenext parameter set is also not ideal, due to the fact that such a searchis a recipe creation time killer, and often cannot provide optimalresults.

According to certain embodiments of the present disclosure, it isproposed to select the second parameter set automatically using anoptimization method. Optimization generally refers to the selection of abest element (with regard to a certain criterion) from a set ofavailable alternatives. An optimization problem generally includesmaximizing or minimizing a function by systematically choosing inputvalues from within an allowed set, and computing the value of thefunction. In some embodiments of the present disclosure, optimization isused to search in the multi-dimensional parameter space and select thenext best parameter set from all the possible combinations. Variousoptimization algorithms can be used for this purpose, e.g., theNelder-Mead method, genetic optimization algorithm, etc.

Referring now to FIG. 8, there is illustrated an example of selectingthe optimal parameter set using a Nelder-Mead optimization method inaccordance with certain embodiments of the presently disclosed subjectmatter.

The Nelder-Mead (NM) method is a direct search method which can be usedto find the minimum or maximum of an objective function in amultidimensional space. The NM method uses the concept of a simplex,which is a special polytope of n+1 vertices in n dimensions, toapproximate a local optimum of a problem with n variables. By way ofexample, the method extrapolates the behavior of the objective functionmeasured at each vertex (test point) in order to find a new test point,and to replace one of the old test points with the new one. Forinstance, the worst point can be replaced with a point reflected throughthe centroid of the remaining n points. If the new point is better thanthe current best point, then the method can continue to search for anoptimal point along this direction. On the other hand, if this new pointis not much better than the previous value, then the simplex can beshrunk towards a better point.

FIG. 8 illustrates an example of a simplex 802 of 3 vertices {P1, P2,P3} in two-dimensions. Each vertex of the simplex represents arespective parameter set (recipe parameters with respective values)generated based on the first parameter set (e.g., default parameterset).

For each vertex of the simplex in the parameter space, the precisiontest and the sensitivity test can be performed in accordance with themetrology recipe configured with the respective parameter setrepresented by the vertex, and a score function can be estimated for thevertex. In some embodiments, the optimization algorithms look for globalmaxima. Different score functions can be defined corresponding todifferent ways of calculation of precision score and sensitivity score.By way of example, the score function can be defined as:Score=w1*exp(−Precision {circumflex over ( )}2/sigma 1{circumflex over( )}2)+w2*exp(−(gain−1){circumflex over ( )}2/sigma 2{circumflex over( )}2), where w1, w2, sigma_1, sigma_2 are empirical parameters of thescore function, and the best precision which is close to 0 andsensitivity gain which is close to 1 correspond to a maximal score ofthe score function (the precision and sensitivity gain can be calculatedin accordance with the above description with reference to blocks 208and 218). In such cases, the score function defined as maximum, is thebest.

In the example of FIG. 8, assuming after the estimation of the scorefunction, the scores for the three vertices are respectively as follows:P1=0.8; P2=0.91; P3=0.92. The vertex of P1 has the worst score, andtherefore will be reflected (e.g., through the centroid of the remainingvertices), forming a new P1, as illustrated in 804. As a result, a newsimplex 806 is generated. The score function is estimated for the newP1, and if the new score is better than the previous score of P1, thenew P1 can be selected as the second parameter set to be used in block222 in FIG. 2A. The metrology recipe is configured with the selectedsecond parameter set, and the process of FIG. 2A goes back to blocks 204and 214 to repeat the first test and second test using the secondparameter set. If the predetermined criterion is still not met at block220, then the above process of selecting the worst vertex (the onehaving the worst score), and reflecting it, can be repeated, and a thirdparameter set can be selected to repeat the process in FIG. 2A. Thisprocess can be iteratively performed until the vertices converge to anoptimal point. The optimal point represents the optimal parameter setthat is eventually selected for generating the metrology recipe. If, ata certain point, the scores of the vertices stop increasing, the simplexcan be scaled (e.g., to a smaller size, as exemplified in 808), and theabove process can continue in the scaled simplex. The scaling can enableto obtain more accurate value of local extremum when the simplex isclose to the optimal point.

Using such an optimization method, an optimal parameter set can beautomatically and efficiently identified, which provides optimal recipeperformance.

It is to be noted that the NM method, as described above, is illustratedfor exemplary purposes, and should not be regarded as limiting thepresent disclosure in any way. Other optimization methods can be used inaddition to or in lieu of the above. The present disclosure is notlimited by the specific optimization method used for selecting the nextparameter sets.

Among advantages of certain embodiments of the recipe creation/tuningprocess as described herein is to generate a simulated image setassociated with ground truth data which enables to perform a sensitivitytest of the metrology recipe, thereby improving the recipe's sensitivityperformance in addition to the precision performance.

Among further advantages of certain embodiments of the recipecreation/tuning process as described herein, is that the recipe tuningprocess is automated by using an optimization algorithm to search forthe optimal parameter set, thereby enabling to shorten thetime-to-recipe and result in optimal recipe performance.

It is to be noted that the illustrated examples are described herein forillustrative purposes, and should not be regarded as limiting thepresent disclosure in any way. Other suitable examples can be used inaddition to, or in lieu of, the above.

It is to be understood that the present disclosure is not limited in itsapplication to the details set forth in the description contained hereinor illustrated in the drawings.

It will also be understood that the system according to the presentdisclosure may be, at least partly, implemented on a suitably programmedcomputer. Likewise, the present disclosure contemplates a computerprogram being readable by a computer for executing the method of thepresent disclosure. The present disclosure further contemplates anon-transitory computer-readable memory tangibly embodying a program ofinstructions executable by the computer for executing the method of thepresent disclosure.

The present disclosure is capable of other embodiments and of beingpracticed and carried out in various ways. Hence, it is to be understoodthat the phraseology and terminology employed herein are for the purposeof description and should not be regarded as limiting. As such, thoseskilled in the art will appreciate that the conception upon which thisdisclosure is based may readily be utilized as a basis for designingother structures, methods, and systems for carrying out the severalpurposes of the presently disclosed subject matter.

Those skilled in the art will readily appreciate that variousmodifications and changes can be applied to the embodiments of thepresent disclosure as hereinbefore described without departing from itsscope, defined in and by the appended claims.

1. A computerized system of generating a metrology recipe usable forexamining a semiconductor specimen, the system comprising a processingand memory circuitry (PMC) configured for: obtaining a first image setcomprising a plurality of first images captured by an examination tool,each first image informative of at least one first image structuralelement (ISE) representing at least one structural element (SE) on thesemiconductor specimen; obtaining a second image set comprising aplurality of second images, wherein each second image is simulated basedon at least one first image and informative of at least one second ISErepresenting the at least one SE, wherein the at least one second ISE ineach second image is resized to a respective scale with reference to theat least one first ISE, and wherein each second image is associated withground truth data related to the respective scale of the at least onesecond ISE; performing a first test on the first image set, comprising:performing a metrology operation on the first image set in accordancewith a metrology recipe configured with a first parameter set, givingrise to a plurality of first measurements corresponding to the pluralityof first images, and calculating a first score indicative of precisionof the metrology recipe based on the plurality of first measurements;performing a second test on the second image set, comprising: performingthe metrology operation on the second image set in accordance with themetrology recipe, giving rise to a plurality of second measurementscorresponding to the plurality of second images, and calculating asecond score indicative of sensitivity of the metrology recipe based onthe plurality of second measurements with respect to the associatedground truth data; and determining, in response to a predeterminedcriterion related to the first score and the second score not being met,to select a second parameter set, configure the metrology recipe withthe second parameter set, and repeat the first test and the second testin accordance with the metrology recipe configured with the secondparameter set.
 2. The computerized system according to claim 1, whereinthe plurality of first images are captured from one or more sites on thesemiconductor specimen.
 3. The computerized system according to claim 1,wherein the PMC is further configured for generating the second imageset, comprising: generating a first design image based on the at leastone first image, the first design image informative of at least onedesign structural element (DSE) corresponding to the at least one ISEand associated with first ground truth data related to a first scale ofthe at least one DSE; generating one or more additional design imageseach informative of the at least one DSE resized to a respective scalewith reference to the first scale, giving rise to a plurality of designimages comprising the first design image and the additional designimages associated with respective ground truth data related torespective scales of the at least one DSE; and using the plurality ofdesign images to generate the second image set, comprising: simulating,based on the plurality of design images, one or more effects caused byone or more physical processes of the semiconductor specimen, givingrise to the plurality of second images associated with the respectiveground truth data.
 4. The computerized system according to claim 3,wherein the simulating comprises: performing a first simulation on theplurality of design images to simulate effects caused by a fabricationprocess of the semiconductor specimen, giving rise to a plurality offirst simulated images; performing a second simulation on the pluralityof first simulated images to simulate effects caused by a scanningprocess of the semiconductor specimen, giving rise to a plurality ofsecond simulated images; performing a third simulation on the pluralityof second images to simulate effects caused by a signal processingprocess of the semiconductor specimen, giving rise to the plurality ofsecond images; and associating the plurality of second images with therespective ground truth data.
 5. The computerized system according toclaim 1, wherein the PMC is configured to calculate the first score bycalculating variance among the plurality of first measurements based ona precision measure.
 6. The computerized system according to claim 1,wherein the PMC is configured to calculate the second score byestimating a linear regression function between the plurality of secondmeasurements and the associated ground truth data, and obtaining thesecond score based on the estimated linear regression function.
 7. Thecomputerized system according to claim 1, wherein the PMC is furtherconfigured to obtain a third image set comprising at least one firstimage, and perform a third test on the third image set, comprising:perform a metrology operation on the third image set in accordance withthe metrology recipe, and calculate a third score indicative ofthroughput of the metrology recipe based on duration of the metrologyoperation.
 8. The computerized system according to claim 1, wherein thefirst parameter set comprises a plurality of recipe parameters assignedwith first values, the recipe parameters selected from a groupcomprising: measurement algorithm parameters and image generationparameters.
 9. The computerized system according to claim 8, wherein thesecond parameter set comprises the plurality of recipe parametersassigned with second values which are selected automatically using anoptimization method.
 10. The computerized system according to claim 1,wherein the PMC is further configured to repeat the first test and thesecond test one or more times in accordance with the metrology recipeconfigured with one or more additional parameter sets, until thepredetermined criterion is met, thereby generating a metrology recipeusable for runtime examination of a semiconductor specimen.
 11. Thecomputerized system according to claim 1, wherein the metrology recipeis defined in accordance with a metrology application selected from agroup comprising: Measurement-Based Inspection (MBI), Critical DimensionUniformity (CDU), Lithography process control, CAD Awareness (CADA), andOverlay (OVL).
 12. A computerized method of generating a metrologyrecipe usable for examining a semiconductor specimen, the methodperformed by a processing and memory circuitry (PMC), the methodcomprising: obtaining a first image set comprising a plurality of firstimages captured by an examination tool, each first image informative ofat least one first image structural element (ISE) representing at leastone structural element (SE) on the semiconductor specimen; obtaining asecond image set comprising a plurality of second images, wherein eachsecond image is simulated based on at least one first image andinformative of at least one second ISE representing the at least one SE,wherein the at least one second ISE in each second image is resized to arespective scale with reference to the at least one first ISE, andwherein each second image is associated with ground truth data relatedto the respective scale of the at least one second ISE; performing afirst test on the first image set, comprising: performing a metrologyoperation on the first image set in accordance with a metrology recipeconfigured with a first parameter set, giving rise to a plurality offirst measurements corresponding to the plurality of first images, andcalculating a first score indicative of precision of the metrologyrecipe based on the plurality of first measurements; performing a secondtest on the second image set, comprising: performing the metrologyoperation on the second image set in accordance with the metrologyrecipe, giving rise to a plurality of second measurements correspondingto the plurality of second images, and calculating a second scoreindicative of sensitivity of the metrology recipe based on the pluralityof second measurements with respect to the associated ground truth data;and determining, in response to a predetermined criterion related to thefirst score and the second score not being met, to select a secondparameter set, configure the metrology recipe with the second parameterset, and repeat the first test and the second test in accordance withthe metrology recipe configured with the second parameter set.
 13. Thecomputerized method according to claim 12, wherein the plurality offirst images are captured from one or more sites on the semiconductorspecimen.
 14. The computerized method according to claim 12, wherein thesecond image set is generated by: generating a first design image basedon the at least one first image, the first design image informative ofat least one design structural element (DSE) corresponding to the atleast one ISE and associated with first ground truth data related to afirst scale of the at least one DSE; generating one or more additionaldesign images each informative of the at least one DSE resized to arespective scale with reference to the first scale, giving rise to aplurality of design images comprising the first design image and theadditional design images associated with respective ground truth datarelated to respective scales of the at least one DSE; and using theplurality of design images to generate the second image set, comprising:simulating, based on the plurality of design images, one or more effectscaused by one or more physical processes of the semiconductor specimen,giving rise to the plurality of second images associated with therespective ground truth data.
 15. The computerized method according toclaim 14, wherein the simulating comprises: performing a firstsimulation on the plurality of design images to simulate effects causedby a fabrication process of the semiconductor specimen, giving rise to aplurality of first simulated images; performing a second simulation onthe plurality of first simulated images to simulate effects caused by ascanning process of the semiconductor specimen, giving rise to aplurality of second simulated images; performing a third simulation onthe plurality of second images to simulate effects caused by a signalprocessing process of the semiconductor specimen, giving rise to theplurality of second images; and associating the plurality of secondimages with the respective ground truth data.
 16. The computerizedmethod according to claim 12, wherein the first score is calculated bycalculating variance among the plurality of first measurements based ona precision measure.
 17. The computerized method according to claim 12,wherein the second score is calculated by estimating a linear regressionfunction between the plurality of second measurements and the associatedground truth data, and obtaining the second score based on the estimatedlinear regression function.
 18. The computerized method according toclaim 12, further comprising obtaining a third image set comprising atleast one first image, and performing a third test on the third imageset, comprising: performing a metrology operation on the third image setin accordance with the metrology recipe, and calculating a third scoreindicative of throughput of the metrology recipe based on duration ofthe metrology operation, wherein the predetermined criterion is furtherrelated to the third score.
 19. The computerized method according toclaim 12, further comprising repeating the first test and the secondtest one or more times in accordance with the metrology recipeconfigured with one or more additional parameter sets, until thepredetermined criterion is met, thereby generating a metrology recipeusable for runtime examination of a semiconductor specimen.
 20. Anon-transitory computer readable storage medium tangibly embodying aprogram of instructions that, when executed by a computer, cause thecomputer to perform a method of generating a metrology recipe usable forexamining a semiconductor specimen, the method comprising: obtaining afirst image set comprising a plurality of first images captured by anexamination tool, each first image informative of at least one firstimage structural element (ISE) representing at least one structuralelement (SE) on the semiconductor specimen; obtaining a second image setcomprising a plurality of second images, wherein each second image issimulated based on at least one first image and informative of at leastone second ISE representing the at least one SE, wherein the at leastone second ISE in each second image is resized to a respective scalewith reference to the at least one first ISE, and wherein each secondimage is associated with ground truth data related to the respectivescale of the at least one second ISE; performing a first test on thefirst image set, comprising: performing a metrology operation on thefirst image set in accordance with a metrology recipe configured with afirst parameter set, giving rise to a plurality of first measurementscorresponding to the plurality of first images, and calculating a firstscore indicative of precision of the metrology recipe based on theplurality of first measurements; performing a second test on the secondimage set, comprising: performing the metrology operation on the secondimage set in accordance with the metrology recipe, giving rise to aplurality of second measurements corresponding to the plurality ofsecond images, and calculating a second score indicative of sensitivityof the metrology recipe based on the plurality of second measurementswith respect to the associated ground truth data; and determining, inresponse to a predetermined criterion related to the first score and thesecond score not being met, to select a second parameter set, configurethe metrology recipe with the second parameter set, and repeat the firsttest and the second test in accordance with the metrology recipeconfigured with the second parameter set.